Technical Field
Embodiments generally relate to a hybrid compression scheme to store synaptic weights in neuromorphic cores.
Discussion
Neuromorphic machines or hardware with spiking neural networks may have shown a high level of energy efficiency when processing real world stimuli, such as in image recognition systems and speech recognition systems. Neuromorphic systems are electronic instantiations of biological nervous systems, which mimic the behavioral and structural aspects of real neural networks. The three main components of neuromorphic systems are neurons (representing processors), synapses (the connection between two neurons), and a learning rule. The neurons have multiple synapses, which convey signals between the neurons. Learning in neuromorphic systems may be realized by adapting the synaptic strength (or synaptic weight) between neurons.
The neuromorphic hardware may typically consist of multiple neuro-synaptic cores that require a large memory capacity. In the neuromorphic cores, the synaptic memory may be used to store multi-bit weights for synaptic connections, and thus requires a large memory capacity. Therefore, a challenge occurs when the weights of the synapses have to be stored in architectures having limited memory capacity.